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log monitoring and alerting system with SMS notifications via Twilio","maintainers":[{"name":"_blackbox-ai_","email":"code@blackbox.ai"}],"readme":"# AI Logs Watcher\n\nAn AI-powered log monitoring and alerting system that uses artificial intelligence to analyze log entries and send SMS notifications via Twilio when critical issues are detected.\n\n## Features Status\n\n### ✅ Available\n- [x] 🤖 **AI-Powered Analysis**: Uses AI to intelligently analyze log entries and detect critical issues\n- [x] ☁️ **Cloud tools**: Server that exposes cloud tools and manage github repository exploration\n- [x] 📱 **SMS Alerts**: Automatic SMS notifications via Twilio for critical and warning conditions\n- [x] ⚡ **Real-time Monitoring**: Configurable polling intervals for continuous log analysis\n- [x] 🎯 **Smart Detection**: Advanced keyword heuristics for detecting critical system failures\n- [x] 📊 **Flexible Log Input**: Support for single logs, multi-line strings, or arrays of log entries\n- [x] 🔧 **Highly Configurable**: Customizable AI models, polling intervals, and alert thresholds\n- [x] 📞 **Voice Interactive Calling**: AI-powered voice calls for critical alerts with interactive responses\n- [x] 📝 **Enhanced Logger**: Drop-in console replacement with automatic AI feeding and optional file writing\n#### Future\n- [ ] 📋 **Phone Contacts Management**: Manage multiple contact lists with priority levels and escalation chains\n- [ ] 🔄 **Alert Fallbacks**: Automatic fallback mechanisms when primary contacts are unavailable\n- [ ] 📈 **Analytics Dashboard**: Real-time monitoring dashboard with alert history and trends\n\n## Installation\n```bash\nnpm install @blackbox_ai/ai-logs-watcher\n```\n\n## Important: Environment Variables Setup\n\n**⚠️ Critical**: This library requires environment variables to be configured in your application. Make sure to call `dotenv.config()` (or your preferred environment configuration method) **before** importing and using the Ailogw package:\n\n```typescript\n// At the top of your application entry point (e.g., index.ts, app.ts)\nimport dotenv from 'dotenv';\ndotenv.config();\n\n// Then import and use Ailogw\nimport { Ailogw } from '@blackbox_ai/ai-logs-watcher';\n\nconst ailogwatch = new Ailogw({\n  // ... your configuration\n});\n```\n\nAlternatively, you can set environment variables directly in your deployment platform (Vercel, Heroku, AWS, etc.) or use other environment management tools.\n\n## Quick Start\n\n```typescript\nimport { Ailogw } from \"./Ailogw/Ailogw\";\n\nconst ailogwatch = new Ailogw({\n    name: \"vercel\",\n    twilio: {\n        numberFrom: \"+17153645914\"\n    },\n    events: {\n        async alert({ options, logs, diagnostic, sendSms }) {\n            switch (diagnostic.raw.status) {\n                case \"warning\":\n                case \"normal\": {\n                    await sendSms(\"+33727926138\", diagnostic.formatted);\n                    return;\n                }\n                case \"critical\": {\n                    await sendSms(\"+33727926138\", diagnostic.formatted);\n                    return;\n                }\n            }\n        }\n    }\n});\n\n// Feed logs to be analyzed\nailogwatch.feedLog(\"2024-09-16 ERROR Database connection failed\");\n```\n\n## Configuration\n\n### Environment Variables\n\nFinal environment variables after completion of the guide\n\n```bash\n## Required for step 2\nAILOGW_TWILIO_ACCOUNT_SID=\"<your_twilio_account_sid>\"\nAILOGW_TWILIO_AUTH_TOKEN=\"<your_twilio_auth_token>\"\nAILOGW_NUMBER_TO=\"<destination_phone_number>\"      # The phone number to send SMS alerts to\nAILOGW_NUMBER_FROM=\"<twilio_phone_number>\"         # Your Twilio phone number (sender)\nELEVENLABS_API_KEY=\"<your_elevenlabs_api_key>\"\n## Required for step 3 (for eleven labs agents and voice)\nAILOGW_ALERT_AGENT_ID=\"<your_alert_agent_id>\"\nAILOGW_GITHUB_AGENT_ID=\"<your_github_agent_id>\"\nBLACKBOX_API_KEY=\"<your_blackbox_api_key>\"\n\n# Optional: disable/enable verbose mode\nAILOGW_LOG=<boolean>\n```\n\n### 1 - Deploy the Tools Server\n\nTo use AI Logs Watcher, you need to deploy the required tools server. You can do this on any cloud platform of your choice (such as Render, Vercel, etc.).\n\n- **Repository:** [fragola-cloud](https://github.com/blackboxaicode/fragola-cloud.git)\n- **Instructions:** Follow the README in the linked repository for deployment steps and required environment variables.\n\nOnce deployed, you will have a server baseURL (e.g., `https://your-tools-server-url.com`) that you will use in the next configuration step.\n\n### 2 - Script to import the elevenLabs agents config to your elevenLabs account (check required envs for step 2 above)\n\nClone the [AI-logs-watcher](https://github.com/blackboxaicode/AI-logs-watcher.git) repository and set up your environment:\n\n```bash\ngit clone https://github.com/blackboxaicode/AI-logs-watcher.git\ncd AI-logs-watcher\ncp .env.example .env   #  ⚠️️ env variables from step 2 must be present.\nnpm install\n```\n\nThen run the ElevenLabs configuration script with your tools server baseURL:\n\n```bash\nnpm run config-elevenlabs <your_tools_server_baseURL>\n```\nAfter running the script, it will output two agent IDs: `AILOGW_ALERT_AGENT_ID` and `AILOGW_GITHUB_AGENT_ID`.  \nCopy both of these values into your `.env` file as shown above.\n\n### 3 - Run the demo server (check required envs for step 3 above)\n\nClone the [server-demo](https://github.com/blackboxaicode/server-demo.git) repository and set up your environment:\n\n```bash\ngit clone https://github.com/blackboxaicode/server-demo.git\ncd server-demo\ncp .env.example .env\n```\n\nCopy all the environment variables from the previous steps into the server-demo `.env` file. Your `.env` should now contain all the variables listed in the \"Final environment variables\" section above:\n\n```bash\n# Complete environment variables needed in server-demo/.env\nAILOGW_TWILIO_ACCOUNT_SID=\"<your_twilio_account_sid>\"\nAILOGW_TWILIO_AUTH_TOKEN=\"<your_twilio_auth_token>\"\nAILOGW_NUMBER_TO=\"<destination_phone_number>\"\nAILOGW_NUMBER_FROM=\"<twilio_phone_number>\"\nELEVENLABS_API_KEY=\"<your_elevenlabs_api_key>\"\nAILOGW_ALERT_AGENT_ID=\"<your_alert_agent_id>\"          # From step 2\nAILOGW_GITHUB_AGENT_ID=\"<your_github_agent_id>\"        # From step 2\nBLACKBOX_API_KEY=\"<your_blackbox_api_key>\"\nAILOGW_LOG=<boolean>\n```\n\nInstall Bun (if not already installed) - see [Bun installation guide](https://bun.com/docs/installation).\n\nInstall dependencies and start the server:\n\n```bash\nbun install\nbun index.ts\n```\n\nThe server will now simulate database failures and call you.\n\n## Usage Examples\n\n### Basic Log Monitoring\n\n```typescript\nconst watcher = new Ailogw({\n    name: \"production-api\",\n    twilio: {\n            numberFrom: \"+17153645914\"\n    },\n    events: {\n        alert: async ({ options, logs, diagnostic, sendSms }) => {\n            if (diagnostic.raw.status === \"critical\") {\n                await sendSms(\"+1234567890\", diagnostic.formatted);\n            }\n        }\n    }\n});\n\n// Single log entry\nwatcher.feedLog(\"2024-09-16 10:30:25 ERROR Database connection failed\");\n\n// Multiple log entries\nwatcher.feedLog([\n    \"2024-09-16 10:30:25 INFO Server started\",\n    \"2024-09-16 10:30:26 WARN High memory usage\",\n    \"2024-09-16 10:30:27 CRITICAL System overload\"\n]);\n```\n\n### Custom Polling Configuration\n\n```typescript\nconst watcher = new Ailogw({\n    name: \"database-logs\",\n    polling: {\n        delay: \"5:minutes\",          // Check every 5 minutes\n        tailAmount: 20               // Analyze last 20 log entries\n    },\n    events: {\n        alert: async ({ options, logs, diagnostic, sendSms }) => {\n            console.log(`Alert Status: ${diagnostic.raw.status}`);\n            if (diagnostic.raw.topErrors) {\n                console.log(\"Critical errors found:\", diagnostic.raw.topErrors);\n            }\n        }\n    }\n});\n```\n\n### Advanced AI Configuration\n\n```typescript\nconst watcher = new Ailogw({\n    name: \"advanced-monitoring\",\n    clientOptions: {\n        baseURL: \"https://api.blackbox.ai\",\n        apiKey: \"sk-your-api-key\"\n    },\n    modelSettings: {\n        model: \"gpt-4\",\n        temperature: 0.1\n        // and any other openai compatible options\n    },\n    events: {\n        alert: async ({ options, logs, diagnostic, sendSms }) => {\n            // Custom alert logic\n        }\n    }\n});\n```\n\n## Log Status Detection\n\nThe AI analyzes logs and categorizes them into three severity levels:\n\n- **🟢 Normal**: Standard operation, no issues detected\n- **🟡 Warning**: Potential issues that should be monitored\n- **🔴 Critical**: Severe problems requiring immediate attention\n\n### Critical Detection Keywords\n\nThe system automatically detects critical issues based on keywords such as:\n- FATAL, PANIC, EMERGENCY, SEVERE\n- OOM, OUT OF MEMORY, SEGFAULT\n- CRASH, KERNEL PANIC, DATA LOSS\n- SECURITY BREACH, DISK FAILURE\n- SERVICE UNAVAILABLE, DEADLOCK\nAnd any other words related to a critical issue\n\n## Error Handling\n\nThe system includes built-in error handling for:\n- Invalid config e.g credentials\n- Invalid time units in polling configuration\n- AI service connectivity issues\n- SMS delivery failures\n\n## API Reference\n\n### Class Methods\n\n#### `feedLog(log: string | string[])`\nFeeds log entries to the watcher for analysis.\n\n#### `activatePolling()`\nStarts or resumes the log polling process.\n\n#### `pausePolling()`\nPauses the log polling process.\n\n#### `get options()`\nReturns the current configuration options.\n\n#### `createLogger(options?: CreateLoggerOptions)`\nCreates an enhanced logger that automatically feeds all log entries to the Ailogw instance for AI analysis while maintaining full console compatibility.\n\n**Features:**\n- 🔄 **Automatic Log Feeding**: Every log call automatically feeds to AI analysis\n- 📁 **File Writing**: Optional file destinations for each log level  \n- 🕒 **Timestamping**: All logs are automatically timestamped\n- ⚡ **Console Compatible**: Drop-in replacement for console object\n- 🛡️ **Error Handling**: Graceful handling of file writing errors\n\n**Parameters:**\n```typescript\ninterface CreateLoggerOptions {\n  destination?: {\n    log?: string;     // File path for general logs\n    error?: string;   // File path for error logs\n    warn?: string;    // File path for warning logs\n    info?: string;    // File path for info logs\n    debug?: string;   // File path for debug logs\n  }\n}\n```\n\n**Examples:**\n\n```typescript\n// Basic usage - automatic AI analysis only\nconst logger = ailogw.createLogger();\nlogger.info(\"Server started successfully\");\nlogger.error(\"Database connection failed\");\n\n// With file destinations - logs written to files AND analyzed by AI\nconst logger = ailogw.createLogger({\n  destination: {\n    info: \"./logs/info.log\",\n    error: \"./logs/errors.log\",\n    warn: \"./logs/warnings.log\",\n    debug: \"./logs/debug.log\"\n  }\n});\n\n// All standard console methods work\nlogger.log(\"General message\");\nlogger.info(\"Information message\");  \nlogger.warn(\"Warning message\");\nlogger.error(\"Error occurred\");\nlogger.debug(\"Debug information\");\n\n// Supports multiple arguments like console\nlogger.error(\"Database error:\", error.message, { userId: 123, timestamp: Date.now() });\n```\n\n**Log Format:**\nAll logs are formatted as: `YYYY-MM-DDTHH:mm:ss.sssZ [LEVEL] your message here`\n\nExample output:\n```\n2024-09-30T14:30:25.123Z INFO Server started successfully\n2024-09-30T14:30:26.456Z ERROR Database connection failed\n```\n\n### Constructor Options\n\n| Name | Type | Default | Description |\n|------|------|---------|-------------|\n| `name*` | `string` | - | Unique identifier for this watcher instance |\n| `twilio*` | `object` | `{}` | Twilio SMS configuration |\n| `twilio.accountSid` | `string` | `process.env.AILOGW_TWILIO_ACCOUNT_SID` | Twilio Account SID |\n| `twilio.authToken` | `string` | `process.env.AILOGW_TWILIO_AUTH_TOKEN` | Twilio Auth Token |\n| `twilio.numberFrom*` | `string` | `process.env.AILOGW_TWILIO_NUMBER_FROM` | Phone number purchased from Twilio (used as \"from\" number) |\n| `activate` | `boolean` | `true` | Whether to automatically start polling when instance is created |\n| `log` | `boolean` | `false` | Whether to enable console logging for this instance |\n| `clientOptions` | `object` | `{}` | Configuration for the AI client (100% Openai compatible) |\n| `modelSettings` | `object` | `{}` | AI model settings for log analysis (100% OpenAI compatible) |\n| `modelSettings.model` | `string` | `\"blackboxai/openai/gpt-4.1-mini\"` | AI model to use |\n| `modelSettings.stream` | `boolean` | `true` | Enable streaming responses |\n| `modelSettings.temperature` | `number` | - | Model temperature setting |\n| `polling` | `object` | `{}` | Polling configuration for log analysis |\n| `polling.delay` | `string \\| number` | `\"10:minutes\"` | Interval between log analysis cycles |\n| `polling.tailAmount` | `number` | `10` | Number of recent log lines to analyze in each cycle |\n| `events*` | `object` | - | Event handlers for different situations |\n| `events.alert*` | `function` | - | Called when an alert is triggered by the AI analysis |\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nThis project license will be available soon.\n\n## Development & Testing\n\n### Running Example Demos\n\nThe repository includes example demos to help you test and understand the voice calling functionality. These examples are not included in the npm package but are available in the repository for development purposes.\n\n#### Prerequisites\n\nBefore running the demos, ensure you have:\n- Cloned the repository: `git clone https://github.com/blackboxaicode/AI-logs-watcher.git`\n- Installed dependencies: `npm install`\n- Set up all required environment variables (see Configuration section above)\n\n#### Available Demos\n\n**1. Voice Demo with Polling (Continuous Monitoring)**\n\nThis demo simulates continuous log monitoring with automatic polling. It feeds logs periodically and triggers voice calls when critical issues are detected.\n\n```bash\nnpx tsx examples/voice-polling.ts\n```\n\nFeatures:\n- Polls logs every 1 second (configurable)\n- Analyzes the last 10 log entries per cycle\n- Automatically triggers voice calls for critical alerts\n- Demonstrates graceful shutdown handling\n\n**2. Voice Demo On-Demand (Single Analysis)**\n\nThis demo performs a one-time analysis of all logs without continuous polling. Useful for testing immediate log analysis and voice call functionality.\n\n```bash\nnpx tsx examples/voice-ondemand.ts\n```\n\nFeatures:\n- Analyzes all logs immediately (no polling)\n- Triggers voice call if critical issues are found\n- Automatically exits after analysis or 5-minute timeout\n- Demonstrates the `triggerAnalysis()` method\n\n#### Demo Requirements\n\nBoth demos require the following environment variables:\n```bash\nAILOGW_NUMBER_TO=\"<destination_phone_number>\"\nAILOGW_NUMBER_FROM=\"<twilio_phone_number>\"\nELEVENLABS_API_KEY=\"<your_elevenlabs_api_key>\"\nAILOGW_ALERT_AGENT_ID=\"<your_alert_agent_id>\"\nAILOGW_GITHUB_AGENT_ID=\"<your_github_agent_id>\"\n```\n\n## Support\n\nFor support and questions, please open an issue on the GitHub repository.\n","readmeFilename":"README.md"}